Resources

We will be posting all lecture materials on the course syllabus. In addition, they will also be listed in the following publicly visible Google drive folder.

Here is a collection of resources that will help you learn more about various concepts and skills covered in the class. Learning by reading is a key part of being a well rounded data scientist. We will not assign mandatory reading but instead encourage you to look at these and other materials. If you find something helpful, post it on Piazza, and consider contributing it to the course website.

Books

Because data science is a relatively new and rapidly evolving discipline there is no single ideal textbook for the course.
Instead we plan to use reading from a collection of books all of which are free.
However, we have listed a few optional books that will provide additional context for those who are interested.

Relevant Classes At Berkeley:

Stat89a: Linear Algebra for Data Science. An introduction to linear algebra for data science. The course will cover introductory topics in linear algebra, starting with the basics; discrete probability and how probability can be used to understand high-dimensional vector spaces; matrices and graphs as popular mathematical structures with which to model data (e.g., as models for term-document corpora, high-dimensional regression problems, ranking/classification of web data, adjacency properties of social network data, etc.); and geometric approaches to eigendecompositions, least-squares, principal components analysis, etc.